Tracking devices exist that sense and track user activities, especially sports activities. An example of a known activity tracking device is a wearable wristwatch device which includes a GPS receiver for tracking and analyzing ‘running’ activity of the user. Another example is a mobile application that utilizes a GPS system of a respective mobile phone for recording movement of users while they exercise. Another example is step counters used in shoes or attached to user clothes to collect numbers of steps taken by the users. However, none of the known tracking devices automatically sense, record, analyze and identify all types of user activities such as walking, running, jogging, cycling, rowing, driving with car, moving with bus, moving with train, walking stairs, running stairs, jumping, swimming, playing football, and skiing.
Nowadays, smart phones are equipped with increasing numbers of sensors such as Global Positioning System (GPS) receivers, accelerometers, and proximity sensors, and users of these smart phones may find it interesting to have mobile applications that can automatically record, sense, analyze, and identify their activities. However, a key challenge in automatic tracking of users' movements for purpose of analyzing types of activity is the classification of activity types. For example, walking vs running activity may have only small difference in respect of collected sensor data. Moreover, for the same activity, the sensor data may vary depending on how the smart phone is carried by the user. For example, the smart phone may be carried by the user in hand, or in pocket or in backpack manners.
Hence, there exists a need for an activity tracking solution, that accurately senses and analyzes all kinds of user activities, and that addresses limitations of known activity tracking solutions.